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#!/usr/bin/env python
import os
import gradio as gr
import PIL.Image
from diffusers.utils import load_image
from model import ADAPTER_NAMES, Model
from utils import (
DEFAULT_STYLE_NAME,
MAX_SEED,
STYLE_NAMES,
apply_style,
randomize_seed_fn,
)
CACHE_EXAMPLES = os.environ.get("CACHE_EXAMPLES") == "1"
def create_demo(model: Model) -> gr.Blocks:
def run(
image: PIL.Image.Image,
prompt: str,
negative_prompt: str,
adapter_name: str,
style_name: str = DEFAULT_STYLE_NAME,
num_inference_steps: int = 30,
guidance_scale: float = 5.0,
adapter_conditioning_scale: float = 1.0,
cond_tau: float = 1.0,
seed: int = 0,
apply_preprocess: bool = True,
progress=gr.Progress(track_tqdm=True),
) -> list[PIL.Image.Image]:
prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
return model.run(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
adapter_name=adapter_name,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
adapter_conditioning_scale=adapter_conditioning_scale,
cond_tau=cond_tau,
seed=seed,
apply_preprocess=apply_preprocess,
)
def process_example(
image_url: str,
prompt: str,
adapter_name: str,
seed: int,
apply_preprocess: bool,
) -> list[PIL.Image.Image]:
image = load_image(image_url)
return run(
image=image,
prompt=prompt,
negative_prompt="",
adapter_name=adapter_name,
seed=seed,
apply_preprocess=apply_preprocess,
)
examples = [
[
"assets/org_canny.jpg",
"Mystical fairy in real, magic, 4k picture, high quality",
"TencentARC/t2i-adapter-canny-sdxl-1.0",
0,
True,
],
[
"assets/org_sketch.png",
"a robot, mount fuji in the background, 4k photo, highly detailed",
"TencentARC/t2i-adapter-sketch-sdxl-1.0",
0,
True,
],
[
"assets/org_lin.jpg",
"Ice dragon roar, 4k photo",
"TencentARC/t2i-adapter-lineart-sdxl-1.0",
0,
True,
],
[
"assets/org_mid.jpg",
"A photo of a room, 4k photo, highly detailed",
"TencentARC/t2i-adapter-depth-midas-sdxl-1.0",
0,
True,
],
[
"assets/org_zoe.jpg",
"A photo of a orchid, 4k photo, highly detailed",
"TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
0,
True,
],
]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Group():
image = gr.Image(label="Input image", type="pil", height=600)
prompt = gr.Textbox(label="Prompt")
adapter_name = gr.Dropdown(label="Adapter", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0])
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True)
negative_prompt = gr.Textbox(label="Negative prompt")
style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
num_inference_steps = gr.Slider(
label="Number of steps",
minimum=1,
maximum=Model.MAX_NUM_INFERENCE_STEPS,
step=1,
value=25,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=30.0,
step=0.1,
value=5.0,
)
adapter_conditioning_scale = gr.Slider(
label="Adapter Conditioning Scale",
minimum=0.5,
maximum=1,
step=0.1,
value=1.0,
)
cond_tau = gr.Slider(
label="Fraction of timesteps for which adapter should be applied",
minimum=0.5,
maximum=1.0,
step=0.1,
value=1.0,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Column():
result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False)
gr.Examples(
examples=examples,
inputs=[
image,
prompt,
adapter_name,
seed,
apply_preprocess,
],
outputs=result,
fn=process_example,
cache_examples=CACHE_EXAMPLES,
)
inputs = [
image,
prompt,
negative_prompt,
adapter_name,
style,
num_inference_steps,
guidance_scale,
adapter_conditioning_scale,
cond_tau,
seed,
apply_preprocess,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name=False,
)
negative_prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name="run",
)
return demo
if __name__ == "__main__":
model = Model(ADAPTER_NAMES[0])
demo = create_demo(model)
demo.queue(max_size=20).launch()
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